SoroushVahidi/scidocs huggingface dataset:
A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis
(2017)

Because facial wrinkle is a representative feature of facial expression, automatic wrinkle detection has been an important and challenging topic for expression simulation, recognition, and animation. Recently, most works about wrinkle detection have focused on permanent wrinkles (e.g., age wrinkles), which are usually linear shapes, whereas the detection of transient wrinkles (e.g., expression wrinkles) has not been sufficiently studied because of their shape diversity and complexity. In this work, a novel algorithm for automatic detection of transient wrinkles with linear, fixed, and chaotic shapes is proposed, which largely consists of edge pair matching, active-appearance-model-based wrinkle structure location, and support-vector-machine-based wrinkle classification. The proposed wrinkle detector is applied for expression synthesis and an improved Poisson wrinkle mapping approach is proposed. Experimental results illustrate the competitiveness of the proposed wrinkle detector in detecting different transient wrinkles. Compared with state-of-the-art algorithms, the proposed approach yields complete and accurate wrinkle centers. The expression synthesized by the improved wrinkle mapping is also much more realistic.
A survey on algorithms of hole filling in 3D surface reconstruction
(2016)

The surface reconstruction of 3D objects has attracted more and more attention for its widespread application in many areas, such as computer science, cultural heritage restoration, medical facilities, entertainment. However, due to occlusion, reflectance, the scanning angle, raw data preprocessing, it is inevitable to lose some point data, which leads to holes in the reconstruction surface, making it undesirable for various applications. Therefore, methods for filling holes in the process of surface reconstruction are critical to the final results of reconstruction. This paper makes a survey of existing well-known hole-filling algorithms, classifies the algorithms into two main categories, analyzes and compares these algorithms from the viewpoints of theories and experimental results to make a clear introduction of their performance. At the end, the paper points out the possible development direction of hole filling in the future and hopes to be a good guide for other researchers.
Generative Adversarial Examples
(2018)

Adversarial examples are typically constructed by perturbing an existing data point, and current defense methods are focused on guarding against this type of attack. In this paper, we propose a new class of adversarial examples that are synthesized entirely from scratch using a conditional generative model. We first train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to model the class-conditional distribution over inputs. Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier. We demonstrate through human evaluation that this new kind of adversarial inputs, which we call Generative Adversarial Examples, are legitimate and belong to the desired class. Our empirical results on the MNIST, SVHN, and CelebA datasets show that generative adversarial examples can easily bypass strong adversarial training and certified defense methods which can foil existing adversarial attacks.
ORCHID : Thai Part-Of-Speech Tagged Corpus
(2009)

This paper presents a procedure in building a Thai part-of-speech (POS) tagged corpus named ORCHID [1]. It is a collaboration project between Communications Research Laboratory (CRL) of Japan and National Electronics and Computer Technology Center (NECTEC) of Thailand. We proposed a new tagset based on the previous research on Thai parts-of-speech for using in a multi-lingual machine translation project. We marked the corpus in three levels:paragraph, sentence and word. The corpus keeps text information in text information line and numbering line which are necessary in retrieving process. Since there are no explicit word/sentence boundary, punctuation and inflection in Thai text, we have to separate a paragraph into sentences before tagging the POS. We applied a probabili stic trigram model for simultaneously word segmenting and POS tagging. Rule for syllable construction is additionally used to reduce the number of candidates for computing the probabili ty. The problems in POS assignment are formalized to reduce the ambiguity occurring in case of the similar POSs.
Low- and high-anxious hypermobile Ehlers–Danlos syndrome patients: comparison of psychosocial and health variables
(2018)

Despite the frequent co-ocurrence of hypermobile Ehler–Danlos syndrome (hEDS) and pathological anxiety, little is known about the psychosocial and health implications of such comorbidity. Our aim was to explore the association between high levels of anxiety and psychosocial (catastrophizing, kinesiophobia, somatosensory amplification, social support and functioning), health (pain, fatigue, BMI, tobacco/alcohol use, depression, diagnosis delay, general health), and sociodemographic factors in people with hEDS. In this cross-sectional study, 80 hEDS patients were divided into two groups according to self-reported anxiety levels: low and high. Psychosocial, sociodemographic and health variables were compared between the groups. Forty-one participants reported a high level of anxiety (51.2%). No differences were found in the sociodemographic variables between high-anxious and low-anxious patients. The percentage of participants with severe fatigue and high depressive symptomatology was significantly higher in the high-anxious group (80.5 vs 56.4; 26.8 vs 12.8%, respectively). High-anxious hEDS patients also showed significantly higher levels of pain catastrophizing, somatosensory amplification as well as a poorer social functioning and general health. Multivariate analyses showed that somatosensory amplification, pain catastrophizing and poor social functioning are variables that increase the probability of belonging to the high-anxious group. Despite limitations, this first study comparing high-anxious versus low-anxious hEDS patients with respect to health aspects, highlight the importance of considering the psychosocial factors (many susceptible to modification), to improve the adjustment to this chronic condition and provide support to those affected through a biopsychosocial approach.
Combining multispectral aerial imagery and digital surface models to extract urban buildings
(2011)

This paper presents an automated classification of buildings in Coleraine, Northern Ireland. The classification was generated using very high spatial resolution data (10 cm) from a Digital Mapping Camera (DMC) for March 2009. The visible to near infrared (VNIR) bands of the DMC enabled a supervised classification to be performed to extract buildings from vegetation. A Digital Surface Model (DSM) was also created from the image to differentiate between buildings and other land classes with similar spectral profiles, such as roads. The supervised classification had the lowest classification accuracy (50%) while the DSM had an accuracy of 81%. The combination of the DSM and the supervised classification achieved an overall classification accuracy of 95%. Two spatial metrics (percentage of the landscape and number of patches) were also used to test the level of agreement between the classification and digitised building data. The results suggest that fine resolution multispectral aerial imagery can automatically detect buildings to a very high level of accuracy. Current space borne sensors, such as IKONOS and QuickBird, lag behind airborne sensors with VNIR bands provided at a much coarser spatial resolution (4m and 2.4m respectively). Techniques must be developed from current airborne sensors that can be applied to new space borne sensors in the future. The ability to generate DSMs from high resolution aerial imagery will afford new insights into the three-dimensional aspects of urban areas which will in turn inform future urban planning. (Received 19th August 2010; Revised 26th January 2011; Accepted 9th February 2011) ISSN 1744-5647 doi:10.4113/jom.2011.1152 51 Journal of Maps, 2011, 51-59 McNally, A.J.D. & McKenzie, S.J.P.
Suffocation using plastic bags: a retrospective study of suicides in Ontario, Canada.
(2000)

One hundred and ten cases of suicidal suffocation using a plastic bag were identified in the files of the Office of the Chief Coroner of Ontario, Canada, between 1993 and 1997. The records were reviewed to determine the demographic characteristics of this group compared with all cases of suicide in Ontario, the scene information, autopsy findings and toxicology results. Most suicides occurred in people over 60 years of age, with older women making up a considerable proportion of cases as compared with other methods of suicide. In 40% of cases the deceased was suffering from a serious illness. Autopsy findings were usually minimal, with facial, conjunctival and visceral petechiae present in a minority of cases. One or more drugs were detected in the blood in 92.6% of cases where toxicologic testing was performed. Benzodiazepines, diphenhydramine and antidepressants were the most common drugs found, with diphenhydramine the most common drug present at an elevated concentration. Information at the scene from "right to die" societies was uncommon. One quarter of decedents took additional measures, besides the use of drugs or alcohol, to ensure the rapidity, certainty or comfort of their death. This study further elucidates the characteristics of this uncommon method of suicide. It emphasizes additional scene findings, such as the presence of dust masks, physical restraints and modification of the plastic bag that may be of use to death investigators in determining the correct manner of death.
Bugs as deviant behavior: a general approach to inferring errors in systems code
(2001)

A major obstacle to finding program errors in a real system is knowing what correctness rules the system must obey. These rules are often undocumented or specified in an ad hoc manner. This paper demonstrates techniques that automatically extract such checking information from the source code itself, rather than the programmer, thereby avoiding the need for a priori knowledge of system rules. The cornerstone of our approach is inferring programmer “beliefs” that we then cross-check for contradictions. Beliefs are facts implied by code: a dereference of a pointer, p, implies a belief that p is non-null, a call to “unlock(l)” implies that l was locked, etc. For beliefs we know the programmer must hold, such as the pointer dereference above, we immediately flag contradictions as errors. For beliefs that the programmer may hold, we can assume these beliefs hold and use a statistical analysis to rank the resulting errors from most to least likely. For example, a call to “spin lock” followed once by a call to “spin unlock” implies that the programmer may have paired these calls by coincidence. If the pairing happens 999 out of 1000 times, though, then it is probably a valid belief and the sole deviation a probable error. The key feature of this approach is that it requires no a priori knowledge of truth: if two beliefs contradict, we know that one is an error without knowing what the correct belief is. Conceptually, our checkers extract beliefs by tailoring rule “templates” to a system – for example, finding all functions that fit the rule template “<a> must be paired with <b>.” We have developed six checkers that follow this conceptual framework. They find hundreds of bugs in real systems such as Linux and OpenBSD. From our experience, they give a dramatic reduction in the manual effort needed to check a large system. Compared to our previous work [9], these template checkers find ten to one hundred times more rule instances and derive properties we found impractical to specify manually.
Scaling regression testing to large software systems
(2004)

When software is modified, during development and maintenance, it is <i>regression tested</i> to provide confidence that the changes did not introduce unexpected errors and that new features behave as expected. One important problem in regression testing is how to select a subset of test cases, from the test suite used for the original version of the software, when testing a modified version of the software. Regression-test-selection techniques address this problem. Safe regression-test-selection techniques select every test case in the test suite that may behave differently in the original and modified versions of the software. Among existing safe regression testing techniques, efficient techniques are often too imprecise and achieve little savings in testing effort, whereas precise techniques are too expensive when used on large systems. This paper presents a new regression-test-selection technique for Java programs that is safe, precise, and yet scales to large systems. It also presents a tool that implements the technique and studies performed on a set of subjects ranging from 70 to over 500 KLOC. The studies show that our technique can efficiently reduce the regression testing effort and, thus, achieve considerable savings.
Predicting risk of software changes
(2000)

Reducing the number of software failures is one of the most challenging problems of software production. We assume that software development proceeds as a series of changes and model the probability that a change to software will cause a failure. We use predictors based on the properties of a change itself. Such predictors include size in lines of code added, deleted, and unmodified; diffusion of the change and its component subchanges, as reflected in the number of files, modules, and subsystems touched, or changed; several measures of developer experience; and the type of change and its subchanges (fault fixes or new code). The model is built on historic information and is used to predict the risk of new changes. In this paper we apply the model to 5ESS® software updates and find that change diffusion and developer experience are essential to predicting failures. The predictive model is implemented as a Web-based tool to allow timely prediction of change quality. The ability to predict the quality of change enables us to make appropriate decisions regarding inspection, testing, and delivery. Historic information on software changes is recorded in many commercial software projects, suggesting that our results can be easily and widely applied in practice.
Predicting Fault Incidence Using Software Change History
(2000)

ÐThis paper is an attempt to understand the processes by which software ages. We define code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: For instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is, on the average, a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight. Index TermsÐFault potential, code decay, change management data, metrics, statistical analysis, generalized linear models.
Chianti: a tool for change impact analysis of java programs
(2004)

This paper reports on the design and implementation of Chianti, a change impact analysis tool for Java that is implemented in the context of the Eclipse environment. Chianti analyzes two versions of an application and decomposes their difference into a set of atomic changes. Change impact is then reported in terms of affected (regression or unit) tests whose execution behavior may have been modified by the applied changes. For each affected test, Chianti also determines a set of affecting changes that were responsible for the test's modified behavior. This latter step of isolating the changes that induce the failure of one specific test from those changes that only affect other tests can be used as a debugging technique in situations where a test fails unexpectedly after a long editing session. We evaluated Chianti on a year (2002) of CVS data from M. Ernst's Daikon system, and found that, on average, 52% of Daikon's unit tests are affected. Furthermore, each affected unit test, on average, is affected by only 3.95% of the atomic changes. These findings suggest that our change impact analysis is a promising technique for assisting developers with program understanding and debugging.
Accurate dB-Linear Variable Gain Amplifier With Gain Error Compensation
(2013)

This paper describes use of a novel exponential approximation for designing dB-linear variable gain amplifiers (VGAs). The exponential function is accurately generated using a simple error-compensation technique. The dB-linear gain is controlled linearly by the gate voltage, resulting in a simple and robust VGA. The proposed dB-linear VGA fabricated in a 65-nm CMOS process achieves a total variable gain range of 76 dB and dB-linear range greater than 50 dB with ±0.5-dB gain error. Under a 1.2-V supply voltage, the current consumption of the VGA is 1.8 mA and that of the output buffer is 1.4 mA. The input-referred in-band noise density is 3.5 nV/√{Hz} and the in-band OIP3 is 11.5 dBm. Due to the very simple circuit topology, the total active area of the VGA and the output buffer is extremely small, 0.01 mm2.
Goubau-Line Leaky-Wave Antenna for Wide-Angle Beam Scanning From Backfire to Endfire
(2018)

A Goubau-line leaky-wave antenna (LWA) with a large scanning angle is presented in this letter. In contrast to the conventional Goubau-line leaky wave with a small scanning angle range, this letter employed a periodically bending Goubau line, which not only brings in a periodic perturbation for leaky-wave radiation, but also enhances the scanning range due to the increased delay for each line element. The simulation and experimental results show that the proposed LWA provides $90\%$ radiation efficiency and 7–10 dBi radiation gain from backfire to endfire through broadside as frequency changes. The proposed antenna features good radiation performance and has a compact and low-profile configuration.
Autonomous NAT Traversal
(2010)

Traditional NAT traversal methods require the help of a third party for signalling. This paper investigates a new autonomous method for establishing connections to peers behind NAT. The proposed method for autonomous NAT traversal uses fake ICMP messages to initially contact the NATed peer. This paper presents how the method is supposed to work in theory, discusses some possible variations, introduces various concrete implementations of the proposed approach and evaluates empirical results of a measurement study designed to evaluate the efficacy of the idea in practice.
Synthesis Design of a Wideband Bandpass Filter With Inductively Coupled Short-Circuited Multi-Mode Resonator
(2012)

In this letter, a class of wideband bandpass filters (BPFs) with short-circuited multi-mode resonators (MMRs) is proposed and designed by a synthesis approach. The MMRs are formed by n sections of cascaded transmission lines, which are all set to be one quarter wavelength long with respect to the center frequency of the passband. Short-ended stubs are then added at the two ends of the MMR as inductive coupling elements. For the filter design, a transfer function is derived to characterize the frequency response of the proposed BPF, and it is enforced to apply to the equal-ripple in-band response. As such, the impedance value for each section is determined once the specifications are given. With this method, design curves of different orders and ripple factors are provided to aid the design of a class of wideband BPFs. As a design example, a fifth-order ultra-wide filter is designed, fabricated and measured in the end. The experimental results verify the design approach.
Inertial sensor-based knee flexion/extension angle estimation.
(2009)

A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 mile/h. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5 mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed.
Suggesting accurate method and class names
(2015)

Descriptive names are a vital part of readable, and hence maintainable, code. Recent progress on automatically suggesting names for local variables tantalizes with the prospect of replicating that success with method and class names. However, suggesting names for methods and classes is much more difficult. This is because good method and class names need to be functionally descriptive, but suggesting such names requires that the model goes beyond local context. We introduce a neural probabilistic language model for source code that is specifically designed for the method naming problem. Our model learns which names are semantically similar by assigning them to locations, called embeddings, in a high-dimensional continuous space, in such a way that names with similar embeddings tend to be used in similar contexts. These embeddings seem to contain semantic information about tokens, even though they are learned only from statistical co-occurrences of tokens. Furthermore, we introduce a variant of our model that is, to our knowledge, the first that can propose neologisms, names that have not appeared in the training corpus. We obtain state of the art results on the method, class, and even the simpler variable naming tasks. More broadly, the continuous embeddings that are learned by our model have the potential for wide application within software engineering.